Head of Data Strategy

The Head of Data Strategy will be a key senior leader within the US Digital, Insights & Analytics community, responsible for building and leading the data strategy capability that enables trusted, scalable, and AI-ready commercialization decision-making. This role will define how commercialization data is acquired, managed, governed, organized, and activated through high-value data products in close partnership with DT (Digital Technology), the global insights community, and senior business stakeholders across the organization. The role will also connect data strategy to meaningful commercialization outcomes, including improved patient pull-through, time-to-therapy, and overall performance.

Key Accountabilities/Responsibilities:  

Lead the commercialization data strategy capability to support growth across the portfolio, including the data acquisition roadmap, governance model, data product strategy, operating rhythms, and enterprise standards required to drive scalable analytics, AI readiness, and commercialization performance. 

• Partner across the commercialization data ecosystem to strengthen data acquisition, data product management, governance, data quality, business enablement, and adoption. 

• Define commercialization data architecture, platform requirements, integration priorities, master data needs, data quality controls, and AI-enablement capabilities in close partnership with DT (Digital Technology) and the global insights community. 

• Provide targeted support to the global insights community and global teams on data strategy, data acquisition, and capability building, helping align approaches, share reusable standards and solutions, and strengthen data foundations across markets. 

• Lead commercialization data acquisition strategy, including identification, evaluation, onboarding, rationalization, and lifecycle management of external and internal data sources to reduce duplication, improve value from data investments, and support analytics, reporting, segmentation, forecasting, AI, and data product development. 

• Establish and maintain commercialization data governance practices, including definitions, ownership, stewardship, quality standards, metadata management, access rules, privacy and compliance considerations, issue-resolution processes, and mechanisms to identify and prevent duplicative or inconsistent data acquisition. 

• Create and manage a portfolio of commercialization data products that translate data into actionable insights and reusable capabilities supporting field execution, access strategy, self-serve analytics, AI-enabled use cases, and commercialization decision-making. 

• Advance AI readiness by ensuring data assets are well-governed, high-quality, discoverable, appropriately documented, interoperable, and fit for analytical, automation, and AI-enabled workflows. 

• Translate commercialization business needs into clear data requirements, product backlogs, delivery priorities, and adoption plans in partnership with Commercial Analytics & Insights, DT, and functional stakeholders. 

• Partner across commercialization communities, including patient, HCP, market access, and medical affairs evidence generation stakeholders, to align data standards, expectations, source strategies, and requirements needed to enable consistent insight generation. 

• Support data fluency and adoption across commercialization teams by improving data usability, clarity, consistency, and accessibility, while helping users understand trusted data sources, definitions, and appropriate use cases across commercialization workflows. 

• Manage key data vendor and partner relationships, including data acquisition agreements, data quality expectations, service levels, documentation, and ongoing performance management. 

• Define and monitor measures of data value, quality, completeness, timeliness, usage, adoption, and readiness for advanced analytics and AI-enabled applications. 

• Collaborate with Commercial Analytics & Insights, DT, and business stakeholders to prioritize data and analytics investments based on commercialization priorities, AI readiness needs, feasibility, risk, and expected business impact. 

• Serve as the senior data strategy subject matter expert and trustedthought partner to commercialization leaders, shaping the data foundation required for launch planning, in-market performance management, pipeline planning, and future digital and AI capabilities.

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